Material emissions testing: VOCs from wood, paint, and insulation materials
Bibliographic record
Abstract
Data from emissions testing conducted by NRC in 2012/2013 of 52 building materials was used to model resulting indoor concentrations in a typical Canadian house (Won et al., 2013). These results helped to provide updated identification of major indoor VOC sources, and led to the selection of paints and wood-based products as priority materials for further evaluation. The significant usage of insulation materials in residential construction, coupled with a lack of emissions data for this product group, led to the selection of insulation as a 3rd class of materials to be studied in greater detail. A total of 30 additional materials were therefore tested for VOC emissions in 2013/14 (10 paints, 8 wood-based materials and 12 insulation products). In accordance with ASTM D5116- 2010, the test method employed small-scale chambers operated for 4 – 8 days under standard conditions (23 C, 50% RH and 1 air change per hour). As with the previous report (Won et al., 2013), the target compounds included 121 VOCs with the emphasis on formaldehyde, acetaldehyde, benzene, and acrolein, concentrations of which in Canadian homes were measured to be higher than indoor air reference levels (IARL) by Health Canada.2 Additionally, different factors that could potentially impact VOC emissions were tested for paint and spray polyurethane foam (SPF). The impact of tinting on emissions from paint was examined to test the hypothesis that dark colored paints tend to have higher emissions than light colored ones. SPF insulation has been reported to emit odorous compounds if it is not applied correctly (Holiday, 2011). The impacts of the SPF application methodology on VOC emissions were tested with a “do-it-yourself” two-component spray foam insulation product. Test specimens were applied either with strict adherence to manufacturer’s instructions or with modifications to application temperature and ratio of two components. This report summarizes the test results with test conditions and material information. To compare emission strength of different materials and compounds, the emission factors measured at 24 hour were used since data sets at later hours (e.g., 96 hour) tend to be subject to bigger analytical uncertainties due to their low concentrations. Indoor air concentrations in Canadian homes were estimated using empirical emission models based on all emission factors measured for the 4 – 8 day test periods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".